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Lung disease classification using deep learning and genetic algorithm

Neural Computing and Applications 2024年 1-16页

作者： Chutia, Upasana Tewari, Anand Shanker Singh, Jyoti Prakash Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Lung disorders are medical conditions that disrupt the lungs and their capacity to function normally. One fatal lung disease is a collapsed lung where the lung collapses partially or fully due to diseases like pneumothorax and atelectasis. The conventional approach to detecting such diseases is time-consuming, often requiring extensive manual analysis by trained experts, leading to delays in diagnosis and treatment. computer-aided diagnostics have the potential to aid doctors in enhancing the consistency of diagnoses while also optimizing time efficiency. In this study, we enhance each lung X-ray image with three image enhancement techniques (i) contrast-limited adaptive histogram equalization (CLAHE), (ii) discrete wavelet transform (DWT), and (iii) gamma correction (GC) in parallel. A 3-channel convolutional neural network (CNN) then uses those enhanced images to extract features. The extracted features are further optimized using a genetic algorithm to improve the efficiency of the classification models. Our proposed model is validated with a dataset containing 9391 X-ray images to achieve an average precision, recall, and F1-score of 98, 97, and 98% and best accuracy of 99.33% which shows an improvement of 2.03% in terms of accuracy over the existing state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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Editor-in-chief’s foreword

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International Journal of computers and Applications 2024年第7期46卷 443-444页

作者： Khan Pathan, Al-Sakib Department of Computer Science and Engineering United International University Dhaka Bangladesh

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Developing ChatGPT for biology and medicine: a complete review of biomedical question answering

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Biophysics Reports 2024年第3期10卷 152-171页

作者： Qing Li Lei Li Yu Li Department of Computer Science and Engineering the Chinese University of Hong Kong

ChatGPT explores a strategic blueprint of question answering(QA) to deliver medical diagnoses, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation of medical domain data via natural language processing(NLP) and multimodal paradigms. By transitioning the distribution of text, images, videos, and other modalities from the general domain to the medical domain, these techniques have accelerated the progress of medical domain question answering(MDQA). They bridge the gap between human natural language and sophisticated medical domain knowledge or expert-provided manual annotations, handling large-scale, diverse, unbalanced, or even unlabeled data analysis scenarios in medical contexts. Central to our focus is the utilization of language models and multimodal paradigms for medical question answering, aiming to guide the research community in selecting appropriate mechanisms for their specific medical research *** tasks such as unimodal-related question answering, reading comprehension, reasoning, diagnosis, relation extraction, probability modeling, and others, as well as multimodal-related tasks like vision question answering, image captioning, cross-modal retrieval, report summarization, and generation, are discussed in detail. Each section delves into the intricate specifics of the respective method under consideration. This paper highlights the structures and advancements of medical domain explorations against general domain methods, emphasizing their applications across different tasks and datasets. It also outlines current challenges and opportunities for future medical domain research,paving the way for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also delineates the course for future probes and utilization in the field of medical question answering.

关键词： ChatGPT Large language models Medical question answering Nature language processing Multimodal paradigms

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ROBOT PATH PLANNING WITH A HYBRID ARITHMETIC OPTIMIZATION ALGORITHM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第3期86卷 197-210页

作者： Song, Qiang Dept. of Computer Science and Software Engineering Zhaoqing University Zhaoqing China

To address the premature convergence and search stagnation of arithmetic optimization algorithm (AOA), the paper proposes a hybrid arithmetic optimization algorithm (HAOA) and applies it to the practical robot path planning (RPP) problem. In initialization phase, the chaotic mechanism is used to create high-quality individuals. In evolution phase, the stochastic disturbance method is applied to enhance the exchange of information between different individuals. The solution space information around the candidate solution was integrated by Levy flight to enhance gene diversity and enhance search performance. In order to better solver the RPP problem, the Spline interpolation method is adapted to the HAOA algorithm for path optimization, so as to smooth the path curve and ensure the planning accuracy. Finally, numeric experiments of benchmark functions and RPP instances verify HAOA’s excellent performance in accuracy and robustness. This research shows arithmetic optimization algorithm can quickly find the global optimal solution, and has fast convergence speed and accuracy. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Stochastic systems

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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OPTIMIZATION RESEARCH OF IMPROVED GENERALIZED NORMAL DISTRIBUTION OPTIMIZATION ALGORITHM IN WSN COVERAGE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第2期85卷 95-110页

作者： Hu, Biling Hou, Mengshu Xie, Fei Liu, Yanan School of Computer science Hefei Normal University China School of Computer Science and Engineering University of Electronic Science and Technology of China China

A coverage control strategy based on an improved generalized normal distribution optimization algorithm is proposed for coverage optimization of sensor networks. Firstly, IGNDO uses a combination of Logistic and Tent chaotic mappings to initialize the original population in the initialization phase;secondly, it uses nonlinear control parameters to adjust the probability of local search and global search being selected in the iterative phase, and perturbs the update of individuals after iterative update with partial Lévy flight to Improve their ability to escape from their local best;Finally, the algorithm’s search ability is further improved by using random regression transgression. In the experimental phase, the improved algorithm is firstly tested with benchmark functions, and secondly IGNDO is employed to sensor network coverage optimization, and the optimal results are obtained in comparison with other optimization algorithms. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Wireless sensor networks

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A Novel WTS-EDC Network for Multi-Source ECG Signal Classification

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Journal of The Institution of Engineers (India): Series B 2024年 1-10页

作者： Rana, Ankur Kumar, Vivek Gupta, Anish Department of Computer Science and Engineering Quantum University Uttarakhand Roorkee247667 India

Electrocardiography (ECG) is a commonly used diagnostic tool in the clinical setting for detecting cardiovascular diseases (CVDs). However, its manual interpretation can be time-consuming and prone to human error. With the recent advancements in deep learning techniques, there has been an increasing interest in using more robust and generalized deep learning algorithms for automated analysis and interpretation of ECG signals. This article provides a novel model, namely Wavelet Time Scattering-Ensembled Dilated Convolution Network (WTS-EDCN), for ECG signal classification. The classifier is an ensemble of dilated convolutional networks, with each network utilizing wavelet time scattering features extracted from multi-source (MS) ECG signal data. The WTS features efficiently constitute the time–frequency characterization of an ECG signal. WTS features are extracted from different ECG signals containing normal and abnormal rhythms. These features are exploited with a deep neural network (DNN) composed of an ensemble of convolutional nets (CNN) with varied dilation rates. Each varied dilation-rated CNN captures the time–frequency information at different scales, and the ensemble ensures the combined use of the information from individual CNNs. The WTS-EDCN model is experimented on MS ECG signals, which have been diagnosed as having either a normal sinus rhythm, Arrhythmia, or congestive heart failure. The model is successful in classifying the diagnosis with high accuracy. These WTS features have proved significant for multi-source ECG signal classification. The EDC classifier reflects a robust and well-generalized classifier since it constitutes an ensembled approach that is a proven strategy for creating better generalized classification models, especially with multi-source datasets. © The Institution of Engineers (India) 2024.

关键词： Electrocardiography

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Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 541-555页

作者： Nandhini, C. Brindha, M. National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion and dynamically set the optimal parameters for developing image compression standards, image restoration algorithms, etc. Insufficient training data with quality scores are a challenge for image quality assessment (IQA) tasks. The existing solutions to deep-convolution-neural-network-based BIQA, which rely on patchwise training, struggle to find an ideal set of patches consistent with the human visual system. To address these issues, HIerarchical paTch Selection (HITS) is proposed. HITS keeps the patches with considerable details in each quadrant according to their intensity variance scatter ratio (IVSR). IVSR identifies the best nonhomogeneous patches by computing patch intensity variance. Extensive trials are conducted, and the performance reveals that the proposed approach achieves excellent performance on synthetic and authentic distortion datasets with less memory and processing power. Moreover, the proposed approach outperforms other BIQA methods that adapt patchwise training strategies. © 2023 IEEE.

关键词： Feature extraction

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Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

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IEEE Transactions on Control of Network Systems 2023年第3期10卷 1245-1254页

作者： Bakhshayesh, Babak Ghaffarzadeh Kebriaei, Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran School of Computer Science Tajrish *** Iran

This article presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This article also takes into account the local and the coupling/infrastructure constraints of EVs, along with transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices. © 2014 IEEE.

关键词： Electric power distribution

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Data-Driven House Price Prediction 3

Data-Driven House Price Prediction

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Shalini, N. Kiran, A Sai Sathwik, B. Reddy, G Trishul Aswini, Kilaru Yadav, M Geetha Computer Science and Engineering Institute of Aeronautical Engineering India Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350375190

This research study analyzes the complex and dynamic field of real estate, aiming to reveal the numerous factors influencing home values. The primary objective of this research study is to develop a prediction system using Random Forest (RF). Leveraging a comprehensive dataset encompassing historical real estate data, this study intends to forecast house values by considering a wide array of variables. Key attributes such as the number of bedrooms and bathrooms, property size, location, and proximity to amenities like transportation, hospitals, and schools form the cornerstone of the proposed model. Drawing insights from past price fluctuations, the proposed algorithm enhances forecast accuracy by analyzing previous value trends. Additionally, for comparative analysis, the proposed approach incorporates multiple regression and linear regression models alongside the Random Forests approach, enabling to assess the performance of our primary model against industry-standard techniques. The resultant system offers users detailed insights into the local real estate landscape alongside precise property price projections. Users can compare specific properties with nearby alternatives, empowering them to make informed decisions. Tailored for real estate agents, investors, and homeowners, our system serves as an invaluable resource for navigating the complexities of real estate transactions. The proposed solution aims to facilitate wise and successful real estate decisions by furnishing precise forecasts and revealing market intricacies. © 2024 IEEE.

关键词： Houses

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